Single-cell RNA transcriptome data present a tremendous opportunity for studying the cellular heterogeneity. Identifying subpopulations based on scRNA-seq data is a hot topic in recent years, although many researchers have been focused on designing elegant computational methods for identifying new cell types; however, the performance of these methods is still unsatisfactory due to the high dimensionality, sparsity and noise of scRNA-seq data. In this study, we propose a new cell type detection method by learning a robust and accurate similarity matrix, named SCCLRR. The method simultaneously captures both global and local intrinsic properties of data based on a low rank representation (LRR) framework mathematical model. The integrated normalized Euclidean distance and cosine similarity are used to balance the intrinsic linear and nonlinear manifold of data in the local regularization term. To solve the non-convex optimization model, we present an iterative optimization procedure using the alternating direction method of multipliers (ADMM) algorithm. We evaluate the performance of the SCCLRR method on nine real scRNA-seq datasets and compare it with seven state-of-the-art methods. The simulation results show that the SCCLRR outperforms other methods and is robust and effective for clustering scRNA-seq data. (The code of SCCLRR is free available for academic https://github.com/wzhangwhu/SCCLRR ).

中文翻译：

---

SCCLRR：一种用于准确聚类单细胞 RNA-seq 数据的稳健计算方法。

单细胞 RNA 转录组数据为研究细胞异质性提供了巨大的机会。基于 scRNA-seq 数据识别亚群是近年来的热门话题，尽管许多研究人员一直专注于设计用于识别新细胞类型的优雅计算方法。然而，由于scRNA-seq数据的高维、稀疏和噪声，这些方法的性能仍然不尽如人意。在这项研究中，我们通过学习一个强大而准确的相似矩阵，称为 SCCLRR，提出了一种新的细胞类型检测方法。该方法基于低秩表示 (LRR) 框架数学模型同时捕获数据的全局和局部内在属性。积分归一化欧几里得距离和余弦相似度用于在局部正则化项中平衡数据的内在线性和非线性流形。为了解决非凸优化模型，我们提出了使用乘法器交替方向法 (ADMM) 算法的迭代优化程序。我们在九个真实的 scRNA-seq 数据集上评估 SCCLRR 方法的性能，并将其与七种最先进的方法进行比较。仿真结果表明，SCCLRR 优于其他方法，并且对于聚类 scRNA-seq 数据具有鲁棒性和有效性。（SCCLRR 的代码是免费提供给学术的 我们在九个真实的 scRNA-seq 数据集上评估 SCCLRR 方法的性能，并将其与七种最先进的方法进行比较。仿真结果表明，SCCLRR 优于其他方法，并且对于聚类 scRNA-seq 数据具有鲁棒性和有效性。（SCCLRR 的代码是免费提供给学术的 我们在九个真实的 scRNA-seq 数据集上评估 SCCLRR 方法的性能，并将其与七种最先进的方法进行比较。仿真结果表明，SCCLRR 优于其他方法，并且对于聚类 scRNA-seq 数据具有鲁棒性和有效性。（SCCLRR 的代码是免费提供给学术的https://github.com/wzhangwhu/SCCLRR ）。